DC
Software Development Engineer
Accepting applicationsDaVinci Commerce · Bengaluru, Karnataka, India
Full-Time Entry AIJavaMentorPythonaI
Posted
29 May
Category
Test
Experience
Entry
Country
India
Job Title: Software Development Engineer (Agentic & LLM Commerce Platform)
Location: Bangalore (Hybrid)
Department: Engineering
Employment Type: Full-time
About the Role
We are looking for a highly experienced Software Development Engineer to help build and enhance our next-generation agentic and LLM-powered commerce platform. In this role, you will architect and implement large-scale data and retrieval systems, build custom partner integrations, and develop AI-driven commerce experiences — from semantic product discovery to autonomous, agent-driven purchasing and decisioning workflows used by global brands.
You’ll collaborate with product, data science, and client engineering teams to design retrieval-augmented generation (RAG) pipelines, vector search infrastructure, and tool-using agents that power conversational and automated commerce at scale — across millions of transactions and interactions daily.
Key Responsibilities
Design, develop, and maintain high-scale data ingestion, transformation, and real-time decisioning systems that feed LLM and agentic commerce workflows.
Build and own semantic search and retrieval infrastructure — including vector databases, embedding pipelines, and hybrid (keyword + semantic) search — powering product discovery and contextual recommendations.
Architect retrieval-augmented generation (RAG) systems and orchestrate tool-using AI agents that can reason over catalogs, execute multi-step commerce tasks, and act autonomously on behalf of users.
Build and own custom integrations with commerce platforms, payment and checkout providers, CDPs, analytics platforms, and customer data APIs.
Improve performance of real-time commerce workflows such as conversational search, dynamic content retrieval, intent understanding, and agentic order orchestration.
Write clean, maintainable, and well-tested code across backend services, data pipelines, and model-serving components.
Partner with data science teams to productionize LLMs and machine learning models, including embedding generation, feature engineering, evaluation, and model-serving components.
Ensure reliability and low latency across distributed systems processing large volumes of events and inference requests in real time.
Mentor engineers, lead design reviews, and drive engineering best practices.
Contribute to continuous improvement in observability, evaluation/guardrails for LLM systems, CI/CD, and deployment automation.
Required Qualifications
1+ years of professional software engineering experience, ideally in commerce, AI/ML, or other data-intensive environments.
Strong proficiency in backend languages such as Java, Scala, or Python.
Hands-on experience building AI-powered features using LLMs — including prompt engineering, RAG, embeddings, or agentic / tool-calling frameworks.
Experience with semantic / vector search and vector databases (e.g., Pinecone, Weaviate, Milvus, pgvector, FAISS, or OpenSearch/Elasticsearch vector capabilities) and embedding models.
Experience with large-scale data processing using technologies like Kafka, Spark, Flink, Beam, or similar.
Hands-on experience with REST/GraphQL APIs, webhooks, OAuth flows, and partner integration frameworks.
Strong understanding of distributed systems, caching, message queues, and low-latency architectures.
Experience with SQL and NoSQL databases for storing high-volume datasets.
Familiarity with cloud platforms (AWS, GCP, or Azure) and container orchestration (Docker, Kubernetes).
Solid understanding of algorithms, data structures, and system design.
Preferred Qualifications
Experience building agentic systems — multi-agent orchestration, tool/function calling, planning, or autonomous workflows (e.g., LangChain, LlamaIndex, LangGraph, or custom frameworks).
Deep experience with retrieval pipelines: chunking strategies, hybrid search, re-ranking, and relevance tuning for semantic search.
Knowledge of the machine learning lifecycle, model deployment, feature stores, real-time inferencing, and LLM evaluation / observability.
Experience in the commerce ecosystem: product catalogs, recommendation systems, checkout/payment flows, identity, event pipelines, or attribution systems.
Familiarity with privacy compliance (GDPR, CCPA) and secure customer data handling, including responsible AI and data governance for LLM systems.
Experience optimizing inference cost and latency (caching, batching, model routing, quantization, or serving frameworks).
Strong communication skills and ability to collaborate across product, engineering, and client-facing teams.
What We Offer
Competitive compensation and benefits
Opportunity to build impactful AI technology used by global brands
Work in an innovative, AI-forward agentic commerce environment
Flexible, hybrid, or remote work options
Growth opportunities in a fast-moving and hig
Show more Show less
Location: Bangalore (Hybrid)
Department: Engineering
Employment Type: Full-time
About the Role
We are looking for a highly experienced Software Development Engineer to help build and enhance our next-generation agentic and LLM-powered commerce platform. In this role, you will architect and implement large-scale data and retrieval systems, build custom partner integrations, and develop AI-driven commerce experiences — from semantic product discovery to autonomous, agent-driven purchasing and decisioning workflows used by global brands.
You’ll collaborate with product, data science, and client engineering teams to design retrieval-augmented generation (RAG) pipelines, vector search infrastructure, and tool-using agents that power conversational and automated commerce at scale — across millions of transactions and interactions daily.
Key Responsibilities
Design, develop, and maintain high-scale data ingestion, transformation, and real-time decisioning systems that feed LLM and agentic commerce workflows.
Build and own semantic search and retrieval infrastructure — including vector databases, embedding pipelines, and hybrid (keyword + semantic) search — powering product discovery and contextual recommendations.
Architect retrieval-augmented generation (RAG) systems and orchestrate tool-using AI agents that can reason over catalogs, execute multi-step commerce tasks, and act autonomously on behalf of users.
Build and own custom integrations with commerce platforms, payment and checkout providers, CDPs, analytics platforms, and customer data APIs.
Improve performance of real-time commerce workflows such as conversational search, dynamic content retrieval, intent understanding, and agentic order orchestration.
Write clean, maintainable, and well-tested code across backend services, data pipelines, and model-serving components.
Partner with data science teams to productionize LLMs and machine learning models, including embedding generation, feature engineering, evaluation, and model-serving components.
Ensure reliability and low latency across distributed systems processing large volumes of events and inference requests in real time.
Mentor engineers, lead design reviews, and drive engineering best practices.
Contribute to continuous improvement in observability, evaluation/guardrails for LLM systems, CI/CD, and deployment automation.
Required Qualifications
1+ years of professional software engineering experience, ideally in commerce, AI/ML, or other data-intensive environments.
Strong proficiency in backend languages such as Java, Scala, or Python.
Hands-on experience building AI-powered features using LLMs — including prompt engineering, RAG, embeddings, or agentic / tool-calling frameworks.
Experience with semantic / vector search and vector databases (e.g., Pinecone, Weaviate, Milvus, pgvector, FAISS, or OpenSearch/Elasticsearch vector capabilities) and embedding models.
Experience with large-scale data processing using technologies like Kafka, Spark, Flink, Beam, or similar.
Hands-on experience with REST/GraphQL APIs, webhooks, OAuth flows, and partner integration frameworks.
Strong understanding of distributed systems, caching, message queues, and low-latency architectures.
Experience with SQL and NoSQL databases for storing high-volume datasets.
Familiarity with cloud platforms (AWS, GCP, or Azure) and container orchestration (Docker, Kubernetes).
Solid understanding of algorithms, data structures, and system design.
Preferred Qualifications
Experience building agentic systems — multi-agent orchestration, tool/function calling, planning, or autonomous workflows (e.g., LangChain, LlamaIndex, LangGraph, or custom frameworks).
Deep experience with retrieval pipelines: chunking strategies, hybrid search, re-ranking, and relevance tuning for semantic search.
Knowledge of the machine learning lifecycle, model deployment, feature stores, real-time inferencing, and LLM evaluation / observability.
Experience in the commerce ecosystem: product catalogs, recommendation systems, checkout/payment flows, identity, event pipelines, or attribution systems.
Familiarity with privacy compliance (GDPR, CCPA) and secure customer data handling, including responsible AI and data governance for LLM systems.
Experience optimizing inference cost and latency (caching, batching, model routing, quantization, or serving frameworks).
Strong communication skills and ability to collaborate across product, engineering, and client-facing teams.
What We Offer
Competitive compensation and benefits
Opportunity to build impactful AI technology used by global brands
Work in an innovative, AI-forward agentic commerce environment
Flexible, hybrid, or remote work options
Growth opportunities in a fast-moving and hig
Show more Show less